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1000 Titel
  • Deriving a Bayesian Network to Assess the Retention Efficacy of Riparian Buffer Zones
1000 Autor/in
  1. Gericke, Andreas |
  2. Nguyen, Hong Hanh |
  3. Fischer, Peter |
  4. Kail, Jochem |
  5. Venohr, Markus |
1000 Erscheinungsjahr 2020
1000 LeibnizOpen
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2020-02-25
1000 Erschienen in
1000 Quellenangabe
  • 12(3):617
1000 FRL-Sammlung
1000 Copyrightjahr
  • 2020
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.3390/w12030617 |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • Bayesian networks (BN) have increasingly been applied in water management but not to estimate the efficacy of riparian buffer zones (RBZ). Our methodical study aims at evaluating the first BN to predict the RBZ efficacy to retain sediment and nutrients (dissolved, total, and particulate nitrogen and phosphorus) from widely available variables (width, vegetation, slope, soil texture, flow pathway, nutrient form). To evaluate the influence of parent nodes and how the number of states affects prediction errors, we used a predefined general BN structure, collected 580 published datasets from North America and Europe, and performed classification tree analyses and multiple 10-fold cross-validations of different BNs. These errors ranged from 0.31 (two output states) to 0.66 (five states). The outcome remained unchanged without the least influential nodes (flow pathway, vegetation). Lower errors were achieved when parent nodes had more than two states. The number of efficacy states influenced most strongly the prediction error as its lowest and highest states were better predicted than intermediate states. While the derived BNs could support or replace simple design guidelines, they are limited for more detailed predictions. More representative data on vegetation or additional nodes like preferential flow will probably improve the predictive power.
1000 Sacherschließung
lokal sediment
lokal nitrogen
lokal nutrient retention
lokal model evaluation
lokal phosphorus
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://orcid.org/0000-0002-9083-947X|https://frl.publisso.de/adhoc/uri/Tmd1eWVuLCBIb25nIEhhbmg=|https://frl.publisso.de/adhoc/uri/RmlzY2hlciwgUGV0ZXI=|https://frl.publisso.de/adhoc/uri/S2FpbCwgSm9jaGVt|https://orcid.org/0000-0002-1248-3113
1000 Label
1000 Förderer
  1. Bundesministerium für Bildung und Forschung |
  2. Ministerium für Umwelt, Klima und Energiewirtschaft Baden-Württemberg |
1000 Fördernummer
  1. 01LC1618A
  2. -
1000 Förderprogramm
  1. BiodivERsA COFUND
  2. Retention of sediments, nutrients, and pesticides in riparian buffer zones
1000 Dateien
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer Bundesministerium für Bildung und Forschung |
    1000 Förderprogramm BiodivERsA COFUND
    1000 Fördernummer 01LC1618A
  2. 1000 joinedFunding-child
    1000 Förderer Ministerium für Umwelt, Klima und Energiewirtschaft Baden-Württemberg |
    1000 Förderprogramm Retention of sediments, nutrients, and pesticides in riparian buffer zones
    1000 Fördernummer -
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6419741.rdf
1000 Erstellt am 2020-04-06T12:56:08.264+0200
1000 Erstellt von 304
1000 beschreibt frl:6419741
1000 Bearbeitet von 122
1000 Zuletzt bearbeitet 2020-06-16T15:21:04.676+0200
1000 Objekt bearb. Mon Apr 06 12:57:20 CEST 2020
1000 Vgl. frl:6419741
1000 Oai Id
  1. oai:frl.publisso.de:frl:6419741 |
1000 Sichtbarkeit Metadaten public
1000 Sichtbarkeit Daten public
1000 Gegenstand von

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